DS5220 Project 1

Instructions

R has excellent visualization capabilities, especially with the ggplot2 package. Please read Chapter 3 of R for Data Science [GW], Garrett Grolemund, Hadley Wickham, and complete the exercises below after you finish each section. Edit the markdown file which came with this html directly. Make sure to enter your R code in the chunks following each question to demonstrate your answers. Follow each code block with a text description of your solution. Answers without demonstration will be given little credit. Code with no description (if requested) will be given little credit.

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

3.2.4 First Steps: Exercises

1. Run `ggplot(data = mpg)`. What do you see?

When running ggplot(data=mpg), all you see is a blank output. ggplot graphics are built by stacking layers with the + operator. Without adding any additional layers to the argument, you will not be able to visualize any data and be given a empty looking coordinate system.

ggplot(data = mpg)

2. How many rows are in mpg? How many columns? Demonstrate how you obtained your answers using R.

To count the number of rows in mpg, you can use the `nrow()` function:

nrow(mpg)

## [1] 234

This function can be used in the following ways:

nrow(df) # returns the total number of rows in the dataframe.

nrow(na.omit(df)) # returns the total number of rows in a dataframe with no NA values in ANY column.

nrow(df[!is.na(df$column_name),]) # returns the total number of rows in a dataframe with no NA values in a SPECIFIC column(s).

To count the number of columns in the mpg dataframe, you can use the `ncol()` or `length()` function:

ncol(mpg)

## [1] 11

length(mpg)

## [1] 11

To get both the number of rows and number of columns in a dataframe, one can use the `dim()` function:

dim(mpg)

## [1] 234  11

This function can also be used to reshape a dataframe

dim(x) <- value # x <- 1:12 ; dim(x) <- c(3,4) would return a dataframe with 3 rows and 4 columns

3. What does the `drv` variable describe? Read the Help Panel in RStudio by typing `?mpg` in the Console Panel to find out. (You will see no output from RMarkdown here.) Produce a description of `drv` by typing `mpg` below.

drv describes “the type of drive train, where f = front-wheel drive, r = rear wheel drive, 4 = 4wd”

?mpg
print("drv - the type of drive train:f = front-wheel drive, r = rear wheel drive, 4 = 4wd")

## [1] "drv - the type of drive train:f = front-wheel drive, r = rear wheel drive, 4 = 4wd"

4. Make a scatterplot of `hwy` vs `cyl` using `geom_point`.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = hwy, y = cyl))

5. What happens if you make a scatterplot of `class` vs `drv`? Why is the plot not useful?

Creating a scatter plot of class vs drv creates a graph which shows a point if there exists any entry in the dataset where a car class has a given drive train. This plot lacks any information besides existence in the data set.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = class, y = drv))

3.3.1 Aesthetic Mappings: Exercises

1. Fix the code in problem 3.3.1.1, and enter it below

The issue with the code provided was that color passed as an argument into the aes() function instead of the geom_point() function.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color="blue")

2. Which variables in `mpg` are categorical? Which variables are continuous? (Hint: type `?mpg` in the Console Panel to read the documentation for the dataset in the Help Panel). How can you see this information when you run `mpg`?

Using ?mpg we can see a desciption of each feature (column) in the mpg dataset under the “Format” section.

The categorical variables in the `mpg` dataset are:

manufacturer - the manufacturer’s name

model - the car model’s name

cyl - the number of cylinders in the engine

trans - they type of transmission

drv - the type of drive train, where f = front-wheel drive, r = rear wheel drive, 4 = 4wd

fl - the fuel type

class - the type of car

The continuous variables in the `mpg` dataset are:

displ - engine displacement, in litres

year - the year of manufacture

cty - city miles per gallon

hwy - highway miles per gallon

mpg

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # ℹ 224 more rows

3. Using your R code for 3.3.1.1, map a continuous variable to `color`, `size`, and `shape`. How does the aesthetic `shape` behave differently for mappings to `fl` and `displ`?

aes(color=column_name)) will differentialy color datapoints based on a selected column, working with both discrete and continuous data.

categorical data will be color scaled so that each category will have a unique color.
continuous data will be color scaled such that there is a color gradient associated with the range of values input.

aes(size=column_name)) will differentialy size datapoints based on a selected column. Size is considered an ordered (continuous data) aesthetic so it will produce a warning when you attept to provide it with an unordered (categorical/discrete) variable.

categorical (discrete) will be plotted with a key defining the sizes mapped to the unique variables. A warning will be displayed as it is reccomended that you do not use categorical variables with the size aesthetic.
continuous data scale will scale the sizes of the points according to the variable’s value. The range of sizes can be adjusted using the range parameter in scale_size_continuous().

aes(shape=column_name)) will differentialy shape datapoints based on a selected column .

categorical data will be mapped and ggplot2 will only use six shapes at a time and other groups will go unplotted.
continuous data does not support mapping to shape directly. If you try to do so, you will get an error because shape must be mapped to a discrete variable. The data must be binned or otherwise converted to a factor before it can be mapped to shape.

# plot 1 (color=displ) continuous variable mapped to color
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color=displ))

# plot 2 (color=drv) continuous variable mapped to color
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color=drv))

# plot 3(size=continuous) continuous variable mapped to size
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size=displ))

# plot4 (size=categorical) 
# Warning: Using size for a discrete variable is not advised.
ggplot(data = mpg) +
  geom_point(mapping = aes(x=displ, y=hwy, size=fl))

## Warning: Using size for a discrete variable is not advised.

# plot 3 (shape=displ) continuous variable mapped to shape
# Produces a warning "A continuous variable cannot be mapped to the shape aesthetic"
# ggplot(data = mpg) + 
#   geom_point(mapping = aes(x = displ, y = hwy, shape=mpg$displ))

# plot 5 (shape=categorical)
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape=fl))

4. What happens if you map the same variable to multiple aesthetics? Use an example from your answers to problem 3.3.1.3.

When you map the same variable to multiple aesthetics (e.g. “shape”, “color” and/or “size”) it can either enhanse or hinder the interpretability of a plot.

Key things to note for mapping the same variable to multiple aesthetics to:

Overcomplication
Redundancy
Clashing Aesthetics
Accessibility
Scale Sensitivity
Legend Clarity
Interpretability of Aesthetics

ggplot(data = mpg) + 
  geom_point(aes(x = year, y = hwy, color = cty, size=cty))

ggplot(data = mpg) + 
  geom_point(aes(x = year, y = hwy, color = class, shape=class))

## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 7. Consider
## specifying shapes manually if you must have them.

## Warning: Removed 62 rows containing missing values (`geom_point()`).

5. What does the `stroke` aesthetic do? What shapes does it work with? (Hint: use`?geom_point`) Try it with `shape=21` and `stroke=displ` in your code from 3.3.1.1.

The stroke aesthetic (default NULL) controls the size of borders around shapes that have borders (shapes 21-24). Stroke requires numeric arguments and can either be a single number (e.g. 2) or a numeric variable set (used below is ). Recommended for continuous variables but does not appear to automatically create a lengend. It will technically accept discrete variables as long as they are numeric but it does not automatically create a warning as size does for discrete variables. stroke may also be considered an ordered (continuous data) aesthetic. Overlapping datapoints also may make visualization unclear as well.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, stroke=displ), shape=21, color='brown') # works with shapes 21-24

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, stroke=cyl), shape=21, color='brown') # works with shapes 21-24

6.What happens if you map an aesthetic to something other than a variable name, like `aes(colour = displ < 5)`? Try this by modifying your code in problem 3.3.1.1.

When trying to map an aesthetic like color to something other than a variable name like aes(colour = displ < 5), you will instruct ggplot to color points based on whether displ is less than 5 or greater than or equal to 5. See below. Depending on the aesthetic mapping you use (shape, size, or color), there may be varying degrees of effectiveness for visualization.

ggplot(data = mpg) + 
  geom_jitter(aes(x = cty, y = hwy, color = displ < 5))

ggplot(data = mpg) + 
  geom_jitter(aes(x = cty, y = hwy, shape = displ < 5))

3.5.1 Facets: Exercises

1. What happens if you facet on a continuous variable?

If you facet_wrap() or facet_grid() on a continuous variable, you will get one subplot for every unique value in the continuous variable. This is pretty useless for continuous variables with more variability in their values, where as if you have a continuous variable (such as year) with a negligible amount of variation, it may not detract from the interpretability of the plot.

#facet_wrap with a discrete variable
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

#facet_wrap with a continuous variable with larger variation 
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ cty, nrow = 2)

#facet_wrap with a continuous variable with negligible variation
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ year, nrow = 2)

#facet_grid with discrete variables
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

#facet_grid with discrete and continuous variable
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ displ)

#facet_grid with continuous variables
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(cty ~ displ)

2. What do the empty cells in plot with `facet_grid(drv ~ cyl)` mean? How do they relate to this plot?

The empty cells in the plot with facet_grid(drv ~ cyl) represent missing values in the geom_point() graph when converted into subplots. Adding facet_grid(drv ~ cyl) to geom_point(mapping = aes(x = drv, y = cyl)) converts each point on the grpah into its own subplot.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = drv, y = cyl))

ggplot(data = mpg) +
  geom_point(mapping = aes(x = drv, y = cyl)) + 
  facet_grid(drv ~ cyl)

3. What plots does the following code make? What does `.` do?

The following code makes a plot of the engine displacement (displ) on the x-axis plotted against the miles per gallon on the highway (hwy) on the y-axis, broken into three subplots horizontally by the type of drive train (drv).

The use of facet_grid(drv ~ .) will specificity the subplots to be stacked vertically as opposed to placed horizontally facet_grid(. ~ drv) according to the drive train.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ drv)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

4. Take the first faceted plot in this section; What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

Faceting Advantages
- Visually clearer comparisons for discrete variables
- For larger datasets, it can help with overplotting by segregating data into subplots
Faceting Disadvantages
- For larger datasets, it can be bad use of space depending on number of facets, a lot to go through. could make individual subplots small and be hard to interpret for viewers.
Color Advantages
- One larger plot to look at that is easier to quickly visually compare different facets of a variable.
- Can be used for different variable types including continuous data better than faceting
Color Disadvantages
- For larger datasets, overplotting points can be a problem leading to interpretaion difficulties
- When there are lots of different categories, the colors may be hard to differentiate.

ggplot(data = mpg) + 
  geom_jitter(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

ggplot(data = mpg) + 
  geom_jitter(mapping = aes(x = displ, y = hwy, color=class))

6. When using `facet_grid()` you should usually put the variable with more unique levels in the columns. Why?

You should usually put the variable with more unique levels in the columns when using facet_grid() because it enhanses the readability by allowing the chart to more likely fit on a single screen and helps with the plot density. This becomes more apparent as the number of unique levels increases, see the example code below and how drastically the interpretability changes:

#facet_grid with a continuous variable with larger variation 
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(. ~ cty)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(cty ~ .)

3.6.1 Geometric Objects: Exercises

1. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

geom_line() : creates line charts

geom_boxplot() : creates boxplots

geom_histogram() : creates histograms

geom_area() : creates area charts

ggplot(data = mpg) +
  geom_line(mapping = aes(x = displ, y = hwy))

ggplot(data=mpg) +
  geom_boxplot(mapping = aes(x = displ))

ggplot(data=mpg) +
  geom_histogram(mapping = aes(x = displ))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data=mpg) +
  geom_area(mapping = aes(x = displ, y = hwy))

2. Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

3. What does `show.legend = FALSE` do? What happens if you remove it? Why do you think I used it earlier in the chapter?

Applying show.legend = FALSE to a geom function call ensures that the aesthetic mappings applied to that geom are not represented in the plot’s legend.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point(show.legend=FALSE) + 
  geom_smooth(se = FALSE, show.legend=FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

4. What does the `se` argument to `geom_smooth()` do?

se - Displays confidence interval around smooth (TRUE by default, see level to control.)

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = TRUE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

5. Will these two graphs look different? Why/why not?

No these graphs will not look different because you are passing the aesthetics into the global ggplot object, and for the second you are passing the aesthetics individually into each layer which results in the same outcome.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

ggplot() + 
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

6. Recreate the R code necessary to generate the following graphs.

# plot 1
ggplot(data=mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(se=FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# plot 2
ggplot(data=mpg, mapping = aes(x = displ, y = hwy, group = drv)) +
  geom_point() +
  geom_smooth(se=FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# plot 3
ggplot(data=mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  geom_smooth(se=FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# plot 4
ggplot(data=mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = drv)) +
  geom_smooth(se=FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# plot 5
ggplot(data=mpg, mapping = aes(x = displ, y = hwy, linetype = drv)) +
  geom_point(mapping = aes(color = drv)) +
  geom_smooth(se=FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# plot 6
ggplot(data=mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(fill = drv), color='white', shape=21, stroke = 2, size = 3)

3.7.1 Statistical Transformations: Exercises

1. What is the default geom associated with `stat_summary()`? How could you rewrite the previous plot to use that geom function instead of the stat function?

the default geom associated with stat_summary() is geom = "pointrange".

ggplot(data=mpg) +
  stat_summary(aes(x=cty, y=hwy))

## No summary function supplied, defaulting to `mean_se()`

## Warning: Removed 3 rows containing missing values (`geom_segment()`).

# rewriting the function to use a geom function instead of a 
ggplot(data = mpg) +
  geom_pointrange(aes(x = cty, y = hwy), stat = "summary")

## No summary function supplied, defaulting to `mean_se()`

## Warning: Removed 3 rows containing missing values (`geom_segment()`).

2. What does `geom_col()` do? How is it different to `geom_bar()`?

geom_col() is used to create bar plots where the height of the bar represents values in the data. It expects the data to be pre-summarized or to contain an explicit y value for each bar.

geom_bar() is used to create bar plots where the height of the bar represents counts of cases or frequencies. It is designed to work with raw data and automatically counts occurrences for categorical variables.

ggplot(data = mpg, aes(x = class)) +
  geom_bar()

ggplot(data = mpg, aes(x = class, y = hwy)) +
  geom_col()

3. Most geoms and stats come in pairs that are almost always used in concert. Read through the documentation and make a list of all the pairs. What do they have in common?

(source for information below: https://ggplot2-book.org/layers.html#stat )

Geometric Objects:

Graphical primitives:
- geom_blank(): display nothing. Most useful for adjusting axes limits using data.
- geom_point(): points.
- geom_path() : paths.
- geom_ribbon() : ribbons, a path with vertical thickness.
- geom_segment(): a line segment, specified by start and end position.
- geom_rect(): rectangles.
- geom_polygon(): filled polygons.
- geom_text(): text.
One variable:
- Discrete:
  - geom_bar(): display distribution of discrete variable.
- Continuous:
  - geom_histogram(): bin and count continuous variable, display with bars.
  - geom_density() : smoothed density estimate.
  - geom_dotplot() : stack individual points into a dot plot.
  - geom_freqpoly() : bin and count continuous variable, display with lines.
Two variables:
- Both continuous:
  - geom_point(): scatterplot.
  - geom_quantile(): smoothed quantile regression.
  - geom_rug(): marginal rug plots.
  - geom_smooth(): smoothed line of best fit.
  - geom_text(): text labels.
- Show distribution:
  - geom_bin2d() : bin into rectangles and count.
  - geom_density2d() : smoothed 2d density estimate.
  - geom_hex(): bin into hexagons and count.
- At least one discrete:
  - geom_count(): count number of point at distinct locations
  - geom_jitter(): randomly jitter overlapping points.
- One continuous, one discrete:
  - geom_bar(stat = "identity"): a bar chart of precomputed summaries.
  - geom_boxplot(): boxplots.
  - geom_violin(): show density of values in each group.
- One time, one continuous:
  - geom_area(): area plot.
  - geom_line(): line plot.
  - geom_step(): step plot.
- Display uncertainty:
  - geom_crossbar(): vertical bar with center.
  - geom_errorbar(): error bars.
  - geom_linerange(): vertical line.
  - geom_pointrange(): vertical line with center.
- Spatial:
  - geom_map(): fast version of geom_polygon() for map data.
Three variables:
- geom_contour(): contour plots.
- geom_tile(): tile the plane with rectangles.
- geom_raster(): fast version of geom_tile() for equal sized tiles.

Statatistical Transformations and Related Geometric Objects:

stat_bin(): related to geoms focused on counting and binning mechanisms
- geom_bar()
- geom_freqpoly()
- geom_histogram()
stat_bin2d(): designed for visualizing the distribution of data points over a two-dimensional space
- geom_bin2d()
stat_bindot(): visualize data distributions using dot plots
- geom_dotplot()
stat_binhex(): designed for visualizing data distributions over two dimensions using hexagonal binning
- geom_hex()
stat_boxplot(): designed for creating box plots that are useful for visualizing the distribution of a dataset
- geom_boxplot()
stat_contour(): designed for creating contour plots that are used to visualize three-dimensional data in two dimensions using contour lines
- geom_contour()
stat_quantile(): allows for the visualization of relationships between variables across different quantiles of the response variable distributions
- geom_quantile()
stat_smooth(): goal of adding a smoothed conditional mean line, or a more general regression line, to a plot
- geom_smooth()
stat_sum(): designed to show density of observations in a scatter plot
- geom_count()

Statistical Transformations that have no correlated geom_ function:

stat_ecdf(): compute a empirical cumulative distribution plot.
stat_function(): compute y values from a function of x values.
stat_summary(): summarise y values at distinct x values.
stat_summary2d(), stat_summary_hex(): summarise binned values.
stat_qq(): perform calculations for a quantile-quantile plot.
stat_spoke(): convert angle and radius to position.
stat_unique(): remove duplicated rows.

4. What variables does `stat_smooth()` compute? What parameters control its behaviour?

parameters controling stat_smooth() are (reference ?stat_smooth):

method : the smoothing function to use:
- lm :
- glm :
- gam :
- loess :
- NULL : the smoothing method is chosen based on the size of the largest group (across all panels)
formula : formula to us in smoothing function (NULL by default, implying y ~ x for Obs < 1000 and y ~ s(x, vs = "cs") for Obs > 1000)
se : logical argument (default = TRUE) to display confidence interval around the smooth
na.rm : logical argument (default = FALSE) which when false, gives a warning when removing missing values, and if TRUE removes them without displaying a warning
orientation : The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either “x” or “y”. See the Orientation section for more detail.
show.legend : logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.
inherit.aes : If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn’t inherit behaviour from the default plot specification, e.g. borders().

geom, stat Use to override the default connection between geom_smooth() and stat_smooth().

n : Number of points at which to evaluate smoother.
span :Controls the amount of smoothing for the default loess smoother. Smaller numbers produce wigglier lines, larger numbers produce smoother lines. Only used with loess, i.e. when method = “loess”, or when method = NULL (the default) and there are fewer than 1,000 observations.
fullrange : If TRUE, the smoothing line gets expanded to the range of the plot, potentially beyond the data. This does not extend the line into any additional padding created by expansion.
level :Level of confidence interval to use (0.95 by default).
method.args : List of additional arguments passed on to the modelling function defined by method.

stat_smooth() computes :

y: the predicted y value on the y-axis for the smooth line
x: the x value used for the y prediction (directly taken from data)
y_min: lower pointwise confidence interval around the mean
y_max: the upper pointwise confidence interval around the mean
se: standared error of the prediction

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method='lm')

## `geom_smooth()` using formula = 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method='glm')

## `geom_smooth()` using formula = 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method='gam')

## `geom_smooth()` using formula = 'y ~ s(x, bs = "cs")'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method='loess')

## `geom_smooth()` using formula = 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method=NULL)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

5. In our proportion bar chart, we need to set `group = 1`. Why? In other words what is the problem with these two graphs?

the group argument is responsible for telling ggplot to treat the data as a single group for the computation of proportion. Without specifying group=1 ggplot might try to calculate proportions separately for different subsets of the data which can lead to incorrect figures.

# provided example 1:
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = after_stat(prop)))

# provided example 1 with group = 1:
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, y = after_stat(prop), group = 1))

# provided example 2:
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = color, y = after_stat(prop)))

# provided example 2 with group = 1:
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = color), position = "fill", group=1)

3.8.1 Position Adjustments: Exercises

1. What is the problem with this plot? How could you improve it?

This plot is not displaying overlapping datapoints giving a incorrect sense to viewers of the density of the data. You can inprove the interpretability by using geom_jitter() instead of geom_point() alone or include position='jitter' as an argument passed into geom_point(), (e.g. geom_point(position='jitter') )

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_point(position='jitter')

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_point(position='jitter')

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_jitter(width=0.4, height=0.4)

2. What parameters to `geom_jitter()` control the amount of jittering?

position = “jitter” by default for geom_jitter
width and height : these arguments control the amount of vertical and horizontal jitter which is added in both positive and negative directions so the spread is twice the amount entered.
size : will affect the appearence of the jitter at different point sizes

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_jitter(width=0.1, height=0.1)

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_jitter(width=0.2, height=0.2)

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_jitter(width=0.4, height=0.4)

3. Compare and contrast `geom_jitter()` with `geom_count()`.

Position vs Size

geom_jitter() : spreads out data points by introducting random noise to show each point individually (each with same size point).
geom_count() : combines data values at the same points and scales the points by size to convey density to the viewer. There is no random noise in the x or y direction in this type of plot.

Data Integrity

geom_jitter() : this can obscure the true x and y values for datapoints due to the random noise introduced
geom_count() : This will not obscure the x and y values for given datapoints because there is no randomness involved in plotting

Density Indication

geom_jitter() : This is more effective for more spare datasets, and less effective for dense datasets
geom_count() : This can be more effective for larger datasets with dense datapoints.

ggplot(data=mpg, mapping = aes(x=hwy, y=cty)) +
  geom_jitter()

ggplot(data=mpg, mapping = aes(x=hwy, y=cty)) +
  geom_count()

4. What’s the default position adjustment for `geom_boxplot()`? Create a visualisation of the mpg dataset that demonstrates it.

the default position adjustment for geom_boxplot() is position = "dodge2".

ggplot(data=mpg) +
  geom_boxplot(aes(x=hwy))

ggplot(data=mpg) +
  geom_boxplot(mapping = aes(x=hwy), position='dodge2')

3.9.1 Coordinate systems: Exercises

1. Turn a stacked bar chart into a pie chart using `coord_polar()`.

Using the code from

ggplot(data = diamonds) + 
  geom_bar(
    mapping = aes(x = cut, fill = cut), 
    show.legend = FALSE,
    width = 1
  ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL) +
  coord_polar()

2. What does `labs()` do? Read the documentation.

labs() is a

Useful arguments from the documentation (?labs): - title : The text for the title.

subtitle : The text for the subtitle for the plot which will be displayed below the title.
caption : The text for the caption which will be displayed in the bottom-right of the plot by default.
tag : The text for the tag label which will be displayed at the top-left of the plot by default.
alt, alt_insight : Text used for the generation of alt-text for the plot. See get_alt_text for examples.
label : The title of the respective axis (for xlab() or ylab()) or of the plot (for ggtitle()).

Also see:

xlab(label) : label for the x-axis
ylab(label) : label for the y-axis
ggtitle(label, subtitle = waiver()) : plot name

3. What’s the difference between `coord_quickmap()` and `coord_map()`?

both functions project portions of the earth into a 2d graph. coord_map() projections, in general, don’t preserve straight lines so it can require considerable computation. On the other hand coord_quickmap()is a fast approximation that does preserve straight lines. coord_map() works best for smaller areas closer to the equator. (source ?coord_map)

nz <- map_data("nz")

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_quickmap()

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_map()

4. What does the plot below tell you about the relationship between city and highway mpg? Why is `coord_fixed()` important? What does `geom_abline()` do?

This plot shows the essentially linear correlation between city and highway mpg and that highway mpg will always outperform city mpg.

geom_abline() : this geom adds a reference line on the plot such that x = y, which can be useful for viewer interpreation of data.
coord_fixed() : this adjusts the scales for the x and y axis to a cartesian coordinate plane with fixed “aspect ratio” (equidistant values). This is important so that users can more clearly visualize the correlation between two variables, more accurate represent geometric shapes like for maps above, and make spacial distances more interpretable.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point()

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  geom_abline()

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  geom_abline() +
  coord_fixed()

DS5220 Project 1

Shane Hussey

Instructions

3.2.4 First Steps: Exercises

1. Run ggplot(data = mpg). What do you see?

2. How many rows are in mpg? How many columns? Demonstrate how you obtained your answers using R.

To count the number of rows in mpg, you can use the nrow() function:

This function can be used in the following ways:

To count the number of columns in the mpg dataframe, you can use the ncol() or length() function:

To get both the number of rows and number of columns in a dataframe, one can use the dim() function:

This function can also be used to reshape a dataframe

3. What does the drv variable describe? Read the Help Panel in RStudio by typing ?mpg in the Console Panel to find out. (You will see no output from RMarkdown here.) Produce a description of drv by typing mpg below.

4. Make a scatterplot of hwy vs cyl using geom_point.

5. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

3.3.1 Aesthetic Mappings: Exercises

1. Fix the code in problem 3.3.1.1, and enter it below

2. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg in the Console Panel to read the documentation for the dataset in the Help Panel). How can you see this information when you run mpg?

The categorical variables in the mpg dataset are:

The continuous variables in the mpg dataset are:

3. Using your R code for 3.3.1.1, map a continuous variable to color, size, and shape. How does the aesthetic shape behave differently for mappings to fl and displ?

4. What happens if you map the same variable to multiple aesthetics? Use an example from your answers to problem 3.3.1.3.

5. What does the stroke aesthetic do? What shapes does it work with? (Hint: use?geom_point) Try it with shape=21 and stroke=displ in your code from 3.3.1.1.

6.What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)? Try this by modifying your code in problem 3.3.1.1.